59 citations · 196 across the 40 of their papers we have counts for
8 papers · 2 filters
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
Hanzhi Chen, Fabian Manhardt, Nassir Navab +1
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning s…
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Yongzhi Su, Yan Di, Fabian Manhardt +5
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage me…
SPARF: Neural Radiance Fields from Sparse and Noisy Poses
Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt +1
Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the avai…
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Ruida Zhang, Yan Di, Zhiqiang Lou +3
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories. Recent methods harness shape pr…
SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation
Ruida Zhang, Yan Di, Fabian Manhardt +2
Category-level pose estimation is a challenging problem due to intra-class shape variations. Recent methods deform pre-computed shape priors to map the observed point cloud into th…
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Xingyu Liu +2
6D object pose estimation is a fundamental yet challenging problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliabl…
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
Yan Di, Ruida Zhang, Zhiqiang Lou +4
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their application…
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection
Issa Mouawad, Nikolas Brasch, Fabian Manhardt +2
Monocular 3D object detection continues to attract attention due to the cost benefits and wider availability of RGB cameras. Despite the recent advances and the ability to acquire…